(Invited) Structure/Transport Relations in Electrolytes: What Have We Learned from Modeling in the Past Quarter Century?
Bibliographic record
Abstract
The development of highly efficient energy conversion devices that utilize sustainable fuel sources with components that are thermally stable, chemically robust, and economically and environmentally feasible and sensible is an important research trust. The electrolyte in low temperature fuel cells is typically an ion containing polymer: either a proton exchange membrane (PEM) or an anion exchange membrane (AEM). Of the many properties that are critical to the effective function of a membrane as the electrolyte and separator in a fuel cell none is more important than the ionic conductivity. The hydrated morphology of either a PEM or an AEM determines many aspects that govern the transport of species (i.e., H2O, H+(H2O), and/or OH-(H2O)). Transport mechanisms in these soft materials are complex as they involve interactions of the cations and anions, the mobile and tethered ions with the water, and the relaxation dynamics of the polymeric backbones and side chains. Over the past 30 years, numerous theoretical and computational studies have been undertaken with a focus at understanding ion and water transport in PEMs and AEMS. This talk will attempt to review what has been learned over the past 25 years from modeling and simulation on the connections, correlations, and relations of polymer structure and architecture to transport properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".